A force model for nose radius worn tools with a chamfered main cutting edge

Chang, Chung-Shin

doi:10.1016/s0890-6955(98)00006-6

Cited by 12 publications

(7 citation statements)

References 8 publications

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“…It also influences chip curling and modifies chip flow direction. In addition to this, researchers [2] have also observed that the nose radius plays a significant role in the surface finish. Therefore, it can be concluded that any attempt to assess the performance of end milling and development of a good model should involve the radial rake angle and nose radius along with other relevant factors.…”

Section: Introductionmentioning

confidence: 85%

Selection of an optimal parametric combination for achieving a better surface finish in dry milling using genetic algorithms

Reddy

Rao

2005

Int J Adv Manuf Technol

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In machining, coolants improve machinability, increase productivity by reducing tool wear and extend tool life. However, due to ecological and human health problems, manufacturing industries are now being forced to implement strategies to reduce the amount of cutting fluids used in their production lines. A trend that has emerged to solve these problems is machining without fluid -a method called dry machining -which has been made possible due to technological innovations. This paper presents an experimental investigation of the influence of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on machining performance in dry milling with four fluted solid TiAlN-coated carbide end mill cutters based on Taguchi's experimental design method. The mathematical model, in terms of machining parameters, was developed for surface roughness prediction using response surface methodology. The optimization is then carried out with genetic algorithms using the surface roughness model developed and validated in this work. This methodology helps to determine the best possible tool geometry and cutting conditions for dry milling.

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Section: Introductionmentioning

confidence: 85%

Selection of an optimal parametric combination for achieving a better surface finish in dry milling using genetic algorithms

Reddy

Rao

2005

Int J Adv Manuf Technol

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show abstract

“…It also influences chip curling and modifies chip flow direction. In addition to this, researchers [1] have also observed that the nose radius plays a significant role in affecting the surface finish. Therefore the development of a good model should involve the radial rake angle and nose radius along with other relevant factors.…”

Section: Introductionmentioning

confidence: 90%

“…Surface roughness measured, µm R ae Surface roughness estimated, µm S Cutting speed, m/min x 1 Logarithmic transformation of cutting speed x 2 Logarithmic transformation of feed rate x 3 Logarithmic transformation of radial rake angle x 4 Logarithmic transformation of nose radius Y Machining response, µm Y 1 Estimated response based on first order model, µm Y 2 Estimated response based on second order model, µm…”

Section: R Amentioning

confidence: 99%

Selection of optimum tool geometry and cutting conditions using a surface roughness prediction model for end milling

Reddy

Rao

2005

Int J Adv Manuf Technol

110

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Influence of tool geometry on the quality of surface produced is well known and hence any attempt to assess the performance of end milling should include the tool geometry. In the present work, experimental studies have been conducted to see the effect of tool geometry (radial rake angle and nose radius) and cutting conditions (cutting speed and feed rate) on the machining performance during end milling of medium carbon steel. The first and second order mathematical models, in terms of machining parameters, were developed for surface roughness prediction using response surface methodology (RSM) on the basis of experimental results. The model selected for optimization has been validated with the Chi square test. The significance of these parameters on surface roughness has been established with analysis of variance. An attempt has also been made to optimize the surface roughness prediction model using genetic algorithms (GA). The GA program gives minimum values of surface roughness and their respective optimal conditions.

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“…The research identified and reviewed deals mainly with orthogonal cutting of metals. Chang [11] and Usui and Shirakashi [12] have developed force models for cutting with rounded cutting edges. These models accommodate the effect of the rounded cutting edge on the overall process.…”

Section: Orthogonal Cutting Studiesmentioning

confidence: 99%